We propose a novel teacher-student adaptation method based on motion cues for video object segmentation. Our method enables a human teacher to demonstrate objects moving with different transformations and associates them with labels. During inference, our approach can learn to segment the object without manual segmentation annotation. The teacher model is a fully convolutional network that combines motion and appearance, denoted as “Motion+Appearance“. The adapted student model is a one-stream appearance-only fully convolutional network denoted as “Appearance“. Combining motion and appearance in the teacher network allows the creation of pseudo-labels for adapting the student network. Our work is inspired from the semi-supervised on-line method [1]}. This work uses manual segmentation masks for initialization. Instead, our approach tackles a more challenging problem and does not require manual segmentation; it relies on the pseudo-labels provided by the teacher model. Figure REF  shows an overview of the proposed method. The two main reasons behind using the adaptation targets from the teacher model is: (1) The student model is more computationally efficient. The inference and adaptation time for the teacher model is 1.5x of the student model's. The adaptation occurs only once on the first frame, then the more efficient student model can be used for inference. (2) The teacher model can be used to generate pseudo-labels for the potential object of interest. It does not Urequire the human to provide manual segmentation mask during the teaching phase which provides a natural interface to the human. Consequently, the adapted student model can segment the object of the interest whether it is static or moving. If the adapted model was still dependant on optical flow it will only be able to recognize the object in motion.
